In this presentation you will learn about emerging technology and how it can be applied to finance function and how to start your journey of becoming an Adaptive Enterprise.

1. How Machine Learning
Will Transform Finance
Rich Clayton
Vice President Product Strategy
Big Data Analytics
Oracle Corporation
@richardmclayton

2. • What is Machine Learning?
• What are high yield use cases in finance?
• What are the common obstacles?
• How has technology advanced?
• What you do to get started?
In This Session….

3. IDC
Organizations That Analyze All
Relevant Data and Deliver Actionable
Information Will Achieve Extra
$430 Billion in Productivity Gains Over
Their Less Analytically Oriented Peers
by 2020

4. What is Machine Learning?

5. Robotic Bosses Unlikely in Next 10 Years

6. Copyright © 2016 Oracle and/or its affiliates. All rights reserved. |
47% US Employment @ Risk of Automation in Next 20 Years
6
Source: Oxford University
The Nature of Work Will Most Definitely Change

7. Machine Learning
is based on algorithms
that can learn from
data without relying
on rules-based
programming.
McKinsey
The New
Technology
Enabler

8. Machine Learning Goes Beyond Predictive Analytics
Build
Model
Predict
Outcome
Take
Action
Learn/
Adapt
MachineHuman
YESTERDAY’S PREDICTIVE ANALYTICS

9. Machine Learning Goes Beyond Predictive Analytics
Build
Model
Predict
Outcome
Take
Action
Learn/
Adapt
MachineHuman
YESTERDAY’S PREDICTIVE ANALYTICS TODAY’S MACHINE LEARNING
Build
Model
Predict
Outcome
Take
Action
Learn/
Adapt

10. WORK
LIFE
Next Best Offer
Recruiting Best Candidates
Fraud Detection
Supply Chain Automation
Predictive Maintenance
Optimize Payment Terms
Boost Data Center Efficiency
Personalized Shopping
Personalized Medicine
Personalized Entertainment
Improved Navigation
Self-Driving Cars
Virtual Assistants
Wealth Management
Machine Learning:
It’s Everywhere

11. Copyright © 2016 Oracle and/or its affiliates. All rights reserved. |
From the Frontlines of Machine Learning Innovation
Large bank used ML to analyze its collection activities and learned it could
eliminate more than 40% of customer calls with better outcome.
Retail
Telecom
Financial
Services
Global retailer used advanced ML to forecast customer demand cutting
forecasting error in half.
Telecom company found that their ML methods yielded a 75x reduction in
‘false-alarms’ for churn; focusing resources on those truly at risk of leaving.

12. Confluence of Events Happening Simultaneously

13. • Predictive models will improve over time
• Business Applications will embed self-learning algorithms
• From experiment designer to process configurator
• Monitor models in production
• Scale labs beyond prototypes
• Operationalize insights
The Role of the Data Scientist Will Change
Source: Forrester, Massive Machine Learning

14. MACHINE AUTOMATION
Simplify execution of
repetitive
computational,
statistical processes
PEOPLE JUDGEMENT
Model inputs factors
and training data,
improve data
imperfections,
model usage (ethics)
Adaptive Intelligence – Best of Both Worlds

15. Consolidate
Many Sources
Interactive
Visualization
Defined
Models
Adaptive Intelligence Platform
Human to Machine
Auto Explain Contextual
Insights
Self-Learning
Algorithms
Programmatic
Integration
Recommend
Best Action
Machine to Human
15

16. How can ML be applied at all levels
of the finance organization?

17. Copyright © 2016 Oracle and/or its affiliates. All rights reserved. |
Framing the Opportunity Space
Process
Transformation
Enterprise
Protection
Finance
Productivity
ML• Employee safety
• Consumer fraud
• Employee theft
• Physical & Cyber-security
• Optimized Payment Terms
• Best-Fit Suppliers
• Optimized Demand Sensing
• Inventory Management
• Autonomous Discovery
• Narrative Automation
• Expense Audit
• Tax Analytics
• Smart Forecasts
• Account Reconciliations

18. What are the common obstacles?

19. “Bottleneck is
management,
implementation
and imagination.”
Andrew McAfee
Principal Research Scientist
MIT Sloan School of Management
Source: HBR, The Business of Artificial Intelligence

20. Copyright © 2016 Oracle and/or its affiliates. All rights reserved. |
Hurdles to Overcome on the Path to ML Adoption
Oracle Confidential – Internal
20
Accessing &
managing data
Disparate
technologies
Missing skill
sets
Bringing together
enterprise and third-party
data meaningfully Enterprises do not have
the technologists and
data scientists needed
Non-integrated stack
within the enterprise
presents challenges
Limited data, limited skills and complex technology stack make ML adoption difficult

21. How is ERP embracing Machine Learning?

22. • Purpose-Built and Ready-To-Go
• Enriched with 3rd-Party Data
• Bundled Decision Science
• Built on the Modern Cloud
• Connected Intelligent Outcomes
Oracle’s
Adaptive
Applications 1st-Party Data
Connected Outcomes
Decision Science Machine Learning
3rd-Party Data
Optimized
Supplier Terms
Demand Sensing
Next Best Offers
and Actions
Best-Fit
Candidates
ERP SCM HCMCX

23. How Would ML Work in an ERP Process?
Insights
Discount Offers
Accepted / Rejected,
Financial Impact
Supervisory Controls
Program Definition,
Supplier Eligibility,
Supplier Opt-in
Decision Science
Artificial Intelligence,
Machine Learning Algorithms
Oracle ERP Cloud
Feedback
Discount Offers
Accepted / Rejected
Surfaced Outputs
Stack Ranked Supplier
Eligibility and Discount Offers
1st-Party Data
Payables, Suppliers, Treasury
Position,
3rd-Party Data
Company Profiles, Business News,
Regulatory Actions,
Channel Delivery
Web, Email, Mobile,
Digital

24. How are analytics platforms embedding machine
learning to serve finance?

25. Image Recognition: More Room to Grow
Muffin or Chihuahua Apple or Owl
[Both are sweet; only one is good to eat]

26. Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 26
Designers Are Thinking About….
• Why do you receive 65 push notifications per day
on your cell phone but 0 from your business?
• What your analytics systems are doing while you
are sleeping?
• Why you spend so much time explaining simple
variances when much can be automated?
• Why you can ask our mobile for directions
to find nearest restaurant but you can’t
ask our system how revenues are
trending in Italy?

27. Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 27
Oracle Analytics Cloud
Autonomous Data Discovery
• In-line Machine Learning for the business
user with no specialty skills required
• Uncovers hidden drivers and recommends areas of
exploration without user actively analyzing specific
factors
27
What is it?
Why does it matter?
• Guide the user to areas of interest they might not
know to look at
• Freedom to explore data more fully without specialist
intervention
• In-depth statistical analysis on contextual data
enriches the interactive analytic experience

28. Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 28
Oracle Analytics Cloud
Self-Learning Contextual Insights
Anticipates questions through self-learning
• Infuse data-based insights into daily activities
• Get customized feeds based on what you are
interested in, when and where you are interested in it,
and who you collaborate with
• Anticipates your needs and delivers appropriate
information to help you make better informed
decisions throughout the day
• Use your device’s voice capabilities to obtain answers
28

29. Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 29
Oracle Analytics Cloud
Voice Activated Conversational Analytics
Powerful, intuitive keyword search
• Interprets semantic layer, user private data, expression
library and catalog artifacts
• Voice-enabled
• Fuzzy match, stemming, natural language processing
• Generates on-the-fly queries - visualizations are
auto-created while user types
• Mobile
• Browser
• Desktop
Available on all platforms
29

30. Copyright © 2017, Oracle and/or its affiliates. All rights reserved. 30
Oracle Analytics Cloud
Natural Language Generation
• NLG as standard visualization type
• Narrative auto-generated based on visual grammar
• Building block for other conversational interface
capabilities
30
What is it?
Why it matters?
• As analytic interfaces become more conversational,
you need words in natural language to enhance the
meaning of visualizations
• Summarizing and highlighting salient points on a
chart allows users to focus on what matters and helps
filter out the noise

31. Oracle Confidential – Internal/Restricted/Highly Restricted
3
How do you get started with ML?

32. Copyright © 2016 Oracle and/or its affiliates. All rights reserved. |
3
Your Action Plan
• Digitize and save all data!
• Create data lab for experimentation
• Form data ethics committee
• Build advanced analytic
competencies and finance
• Embrace cloud; it’s faster!

33. Copyright © 2016 Oracle and/or its affiliates. All rights reserved. |
EXPERIMENT
PROCESS
DISCOVERPREDICT
WRANGLE
• Build a system of innovation,
not just system of record
• Understand data potential
• Enrich the data and make
it better
• Unlock insights and share
the value
• MIT rule of thumb: 6 x 6 x 6
Form a Data Lab: A Fundamental New Approach

34. Copyright © 2016 Oracle and/or its affiliates. All rights reserved. |
Deepen Analytic Competencies in Finance
Storytelling
Scenario Analysis
Analytic Process Design
Visualization
Machine Learning
Data Modeling
Data Wrangling
Text Analysis

35. “Computers are useless. They can only
give you answers.” – Picasso
…so ask smarter questions.